News/Blog

Setting the right tone

I’ve been finding that the problems reviewers sometimes have with my papers is not so much the actual experiments or the conclusions drawn from them, but rather the tone with which they are presented. Take this passage from a paper we’re revising:

To avoid telling “just-so stories”, researchers studying adaptation should actively identify, test, and exclude alternative hypotheses. As George Williams famously put it, “adaptation is a special and onerous concept that should not be used unnecessarily, and an effect should not be called a function unless it is clearly produced by design and not by chance”. Selection is an important mechanism of evolution, but not the only one. Nonadaptive mechanisms like mutation and drift can also play important roles. Mechanisms by which individuals may directly benefit from expressing a trait should also be explored.

This passage seems to evoke strong emotional responses from some people (and not because of the awkward passive voice at the end). I thought we were just describing good scientific practice for studying adaptation. The reviewers apparently thought it was patronizing. One reader even thought that using the phrase “just-so stories”, automatically counted as a full endorsement of Steven J. Gould & Dick Lewontin’s attack on behavioral ecology. The passage seems to make some people really defensive, so I think for the sake of the paper we’re going to take it out. Which is too bad, since I think it’s a message that some researchers could stand to hear (or hear again).

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    Cool paper: Gut inflammation can boost horizontal gene transfer between pathogenic and commensal Enterobacteriaceae

    Plasmids are mobile bits of DNA that play a key role in bacterial evolution. They shuttle genes for things like antibiotic resistance and pathogen virulence among different strains or species of bacteria. But not all plasmids carry these genes, and it’s been an outstanding question how plasmids persist in bacterial populations. One possibility is that they’re a kind of genetic parasite, slightly reducing the fitness of the cells they infect but continually infecting new bacteria. One problem with this idea, though, is that their infection rates often don’t seem high enough for a purely parasitic lifestyle.

    A new paper by Bärbel Stecher and colleagues at ETH Zürich shows that when Salmonella infect mammalian guts they create an environment that drastically increases plasmid transfer among the bacteria there. They inflame the gut tissue, causing a “bloom” of resident E. coli. All those bacteria bump into each other more, allowing plasmids—which spread by direct contact between bacterial cells—to go gangbusters. The paper has a lot of good experiments showing that it’s the increased bacterial density, and not the inflammation, that causes increased plasmid transfer.

    The implication is that plasmids can make a living as parasites if Salmonella and other pathogens cause enough gastrointestinal disturbance, as they might in the developing world or in nonhuman mammal populations. I did find overblown the authors’ claims that their findings “shift the current paradigm” because they show that Salmonella and E. coli share plasmids (which we already knew) and “boost” pathogen evolution (which their findings do not show), but overall this is a pretty cool paper.

    Stecher B, Denzler R, Maier L, Bernet F, Sanders MJ, Pickard DJ, Barthel M, Westendorf AM, Krogfelt KA, Walker AW, Ackermann M, Dobrindt U, Thomson NR & Hardt W-D (2012) Gut inflammation can boost horizontal gene transfer between pathogenic and commensal Enterobacteriaceae. PNAS 109: 1269-1274.

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      What makes something “systems biology”?

      Like many biologists, I’ve wondered at times what the relatively new discipline of systems biology is all about. A lot of things get called systems biology, from genomics to metabolism to gene regulation. I often find the systems biology approaches to these fields pretty interesting, even when it’s fairly removed from my research area. Like myself, systems biologists often have a background in physics. Sometimes systems biology even includes microbial cooperation. So what ties it all together?

      Well, systems biology:

      • studies dynamic, complex systems whose behavior is governed by the interactions of their component parts
      • uses quantitative, data-rich measurements of dynamical behavior
      • uses mathematical and computational models to predict and analyze dynamical behavior

      Viewed this way, I would argue that systems biology has a lot in common with ecology and evolutionary biology. In some ways, it’s just population biology applied to molecules and cells rather than individuals and species. Asking how genetic regulatory circuits create persistent cycles of gene expression rather than coming to some stable equilibrium is not all that different than asking how predator/prey dynamics create population cycles rather than coming to some stable equilibrium. And with any luck, systems biology will help bridge the traditional divide between population biologists and their more mechanistic colleagues.

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        New paper: Tragedy of the commons among antibiotic resistance plasmids

        smith (2012) Tragedy of the commons among antibiotic resistance plasmids. Evolution. doi: 10.1111/j.1558-5646.2011.01531.x | Early view at Evolution

        Abstract: As social interactions are increasingly recognized as important determinants of microbial fitness, sociobiology is being enlisted to better understand the evolution of clinically relevant microbes and, potentially, to influence their evolution to aid human health. Of special interest are situations in which there exists a “tragedy of the commons,” where natural selection leads to a net reduction in fitness for all members of a population. Here, I demonstrate the existence of a tragedy of the commons among antibiotic resistance plasmids of bacteria. In serial transfer culture, plasmids evolved a greater ability to superinfect already-infected bacteria, increasing plasmid fitness when evolved genotypes were rare. Evolved plasmids, however, fell victim to their own success, reducing the density of their bacterial hosts when they became common and suffering reduced fitness through vertical transmission. Social interactions can thus be an important determinant of evolution for the molecular endosymbionts of bacteria. These results also identify an avenue of evolution that reduces proliferation of both antibiotic resistance genes and their bacterial hosts.

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          “A whole dimension of completely weird, incredible things”

          Errol Morris has this “Op-Doc” (Opinion-Documentary, as odd as that sounds) over at the New York Times. Technically, it’s about historical research, but I think the phenomenon holds true for biology, as well. As interviewee Tink Thompson puts it, “if you put any event under the microscope, you will find a whole dimension of completely weird, incredible things going on”. I’ve always loved Morris’ work. A lot of it deals with issues of knowing—how we know what we know, how we sometimes deceive ourselves, what evidence does or doesn’t say. Important issues for any scientist.

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            Contagion

            Scene from ContagionI finally got around to seeing the film Contagion, a realistic portrayal of what a serious viral pandemic would look like in our day and age. Unlike the vast majority of outbreak films, Contagion gets a lot of the science right. The basic reproductive number R0, for example, makes an appearance in the film. The film’s fictional MEV-1 virus was inspired by Nipahvirus. MEV-1 has an R0 of 2-4 and a mortality rate of ~25% — severe, but realistic. Epidemiologist Ian Lipkin consulted on the film.

            My favorite line is when the military asks CDC Deputy Director Lawrence Fishburne if the virus was a bioterrorist attack, like a weaponized bird flu. Fishburne reponds, “No one has to weaponize the bird flu. The birds are doing that.”

            The only part I really disliked about the film is how the virus is described as “mutating” or “changing”, but never “evolving”. Unfortunately, this aspect is also a realistic description of our public health system.

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              Work and labor

              One of the things that seems wierd to me about science as a profession is how the job often requires both esoteric activities like probability theory or genetic engineering and mundane menial labor like washing dishes or mixing large amounts of dirt. As a graduate student I used to want to do everything in the lab myself: making media, counting plates, all of it. Part of it was that I wanted to know how every part of my experiments worked, and part of it was perfectionism and paranoia. Now, I find myself wanting to teach someone how to do that stuff well, and have them do the tedious work for me. Especially the part where you’ve already done the pilot experiments and the first replicate so you already know what the answer’s going to be — you’re just getting clean data for the paper. Now, I’d rather develop the assays, get them working reliably, and then let someone else finish out the data collection.

              I have to say, though: my experience of lab work has recently been completely transformed since purchasing an mp3 player (I’m a slow adopter). Before, it was like, “I need to make media? Again? Grumble grumble…” Now it’s like, “Aw, hell yeah”.

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                Slime molds get their 15 minutes

                Slime molds

                Photo: Steven L. Stephenson

                Dictyostelium and its relatives are getting some time in the limelight from Carl Zimmer in the New York Times. The article even includes some coverage of our lab’s work on the evolution of cooperation.

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                  University of Houston visit

                  Last week I had the opportunity to visit the University of Houston. When our lab was still at Rice I went to a talk or two at UH but never really got much chance to interact with the research groups there. So it was great fun to talk evolutionary genetics with Tim Cooper and his lab, social amoebae with Elizabeth Ostrowski, evolutionary networks with Ricardo Azevedo, behavior and morphology with Tony Frankino, ant behavior with Blaine Cole, and food and art with Dan Graur (over dinner).

                  The audience for my talk was mostly biologists, not necessarily in my field of study, many of whom may not have been ecologists or evolutionary biologists. So I used the opportunity to develop what will hopefully become my job talk. I’ve been inspired by Will Ratcliff’s “Morgan Freeman” philosophy for scientific talks. The idea is that it should be a story, presented with engaging images, and it’s your job to guide the audience through that story with an easy-to-understand narrative.

                  One thing I’ve learned: making slides is no substitute for actually practicing a talk. Even if you mentally run through your narrative as you make slides, when you actually get to talking you’ll inevitably discover places where the narrative isn’t as clear as you thought it was.

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                    New paper: Distinguishing causes of virulence evolution

                    smith j (2011) Distinguishing causes of virulence evolution: Reply to Alizon and Michalakis. Evolution In press. doi:10.1111/j.1558-5646.2011.01428.x | Journal

                    Abstract: In a recent study of the symbiosis between bacteria and plasmids, the available evidence suggests that experimental evolution of plasmid virulence was primarily driven by within-host competition caused by superinfection. The data do not exclude the possibility, however, that a trade-off between virulence and infectious transmission to uninfected bacteria also played a minor role.

                    This one’s an in-print discussion between Samuel Alizon, Yannis Michalakis, and myself. They were worried that some researchers might interpret my earlier paper about plasmid evolution as rejecting the hypothesis that pathogen virulence can be influenced by a trade-off between infectious transmission and virulence. So they made some mathematical models to determine what kinds of evidence we’d need to really rule out the influence of such a trade-off. The work I did doesn’t rule out a trade-off, but it does show that competition among pathogens within hosts was necessary and sufficient to explain how plasmid virulence evolved.

                    I found it nice (and useful) to talk with the authors before we sent in our respective manuscripts. It helped clear up some places where we were using language in a way that could be misinterpreted and to focus on places where there might be real issues. In the end, my impression is that we basically agree about what my paper does and doesn’t show, but they wanted to use it as a sounding board for how we should go about testing virulence theory.

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                      Science as it really happens

                      Electron cafe made this wonderful diagram showing the scientific process as it appears on movies and TV versus the scientific process as actual scientists experience it day-to-day:

                      Science: perception and reality

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                        Cool paper: Interdependence of cell growth and gene expression

                        Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T (2010) Interdependence of cell growth and gene expression: origins and consequences. Science 330: 1099-1102. Journal | PubMed

                        Using extremely simple models of cell physiology, Scott and colleagues are able to predict how unnecessary gene expression reduces bacterial fitness, how growth rate changes due to nutrient quality, and how growth rate is reduced by sublethal concentrations of antibiotics that inhibit translation. They also derive the classic Michaelis-Menten equation for growth rate as a function of resource concentration. Their models assume that growth is linearly proportional to translation rate and that there are three classes of proteins: those used to acquire nutrients, those used to make more protein, and a class that is unaffected by nutrient availability. With this, tons of results pretty much just fall out. The paper is a great example of how systems biology links molecular details to whole-cell phenotypes.

                        “Like Ohm’s law, which greatly expedited the design of electrical circuits well before electricity was understood microscopically, the empirical correlations described here may be viewed as microbial ‘growth laws,’ the use of which may facilitate our understanding of the operation and design of complex biological systems well before all the underlying regulatory circuits are elucidated at the molecular level.”

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                          Choice quotes

                          From the 2011 Microbial Population Biology Gordon Conference:

                          Only believe the reviewers 50% of the time. Fight the reviewers; it’s really good. Unless you’re wrong, and then you’re just being a jerk. —Howard Ochman

                          [It's a] Darwinian principle that nobody likes dead kids. —Mike Turelli

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                            New digs: Washington University in St. Louis

                            The lab’s moved to Washington University in St Louis, and I with them. We’re unpacking and getting set up in our swanky new space.

                            Hopefully the amoebae will act more or less the same as in our previous lab, but I wouldn’t bet too much on it. My experience in Greg Velicer’s lab showed that sometimes experiments can give different results in different labs. Paul Turner found that the culprit is often the water. Knowing this, I spent many late nights back in Houston finishing up a big block of experiments to get a discrete chunk of data collected in only one lab. I’ve yet to analyze it all, but I’ll probably need to do a couple follow-up experiments to get at the cause(s) of the effects there. Hopefully any differences will be minor.

                            I also want to capitalize on Dicty’s photogenicity (by microbial standards) and get a bunch of cool pictures to use in talks, on this site, and maybe even as cover images for journals.

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                              Practical implications of microbial evolution

                              When I originally got interested in microbes, it was mostly for somewhat obscure academic questions about mobile genetic elements: how does evolution solve conflicts to create cohesive individuals instead of loose associations of genes? The abundance of plasmids and phage, and the fact that they sometimes carry genes for antibiotic resistance or pathogen virulence, seemed like a situation where that integration was not fully complete, which made them a good system to study. The fact that microbes and their mobile elements had significant effects on human health was a nice bonus, but not my main motivation. Mostly, I was happy that a lot of the genetic details had already been worked out. I liked the infectious disease aspect, but again mainly for academic reasons about conflict evolution, not because of its practical importance for humans.

                              Now, I find myself finding the applied aspects more compelling. They’re still not the primary thing, but if you have a choice of systems to study for academic reasons, why not study one that’s also relevant to human welfare? I’ve spent the last several years studying microbial cooperation in Myxococcus bacteria and Dictyostelium amoebae. These organisms are pretty cool biologically, but they really don’t have much to do with humans. Other examples of microbial cooperation are maybe more plain but also more relevant, like the Pseudomonas bacteria that kill people with cystic fibrosis.

                              I also recently realized the potential practical implications of my old grad school work on plasmids. In those experiments, I observed a rapid, repeatable loss of antibiotic resistance and a suppressed proliferation of bacteria — both of which are desirable clinical outcomes. I’d never really thought about those results from an applied standpoint. Why did my plasmids evolve to get rid of resistance genes while in other people’s experiments they stuck around even when there were no antibiotics? Is it possible to influence natural plasmid evolution to follow the path I saw? It’s definitely worth following up on.

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                                What happens to data between the experiment and the paper?

                                In my field, data is often some quantitative measure of abundance: How many cells are there? What fraction are this genotype or that genotype? How does the number or fraction change over time? Typically, the raw experimental measurements get put into a spreadsheet, analyzed, and then that’s what gets put into figures or statistics programs.

                                A lot can go on in those spreadsheets, and a lot of mistakes can be made, yet in my experience there’s rarely any discussion or training about what happens to data between the experiment and the paper. At no time in grad school or either of my postdocs did anyone ask to talk to me about my spreadsheets. Over the years, I’ve figured a few things out and caught a lot of potential mistakes, so there are definitely things to discuss. I’ve also noticed some issues the few times I’ve looked closely at other researcher’s spreadsheets. It seems really strange to me that labs can spend so much time getting the experiments right but then be kind of careless with the data.

                                If you were going to teach a new grad student best practice for handling data and calculations, what would you cover? Here are some things that come to mind:

                                • Keeping the raw measurements collected in a single place for easy access later on
                                • Data quality control: making sure you didn’t type in the wrong number, or type it into the wrong place
                                • Annotating data so that you can go back to your lab notebook or original files to verify specific entries
                                • Identifying bad data points that should not be included
                                • When you should average multiple measurements, and how you should do it. Whether you should take the arithmetic or geometric mean, for example.
                                • Making sure that lists of calculated values use the same formula for every entry
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                                  New paper: Superinfection drives virulence evolution in experimental populations of bacteria and plasmids

                                  smith j (2011) Superinfection drives virulence evolution in experimental populations of bacteria and plasmids. Evolution 65: 831-841. Journal | PubMed | Faculty of 1000

                                  Abstract: A prominent hypothesis proposes that pathogen virulence evolves in large part due to a trade-off between infectiousness and damage to hosts. Other explanations emphasize how virulence evolves in response to competition among pathogens within hosts. Given the proliferation of theoretical possibilities, what best predicts how virulence evolves in real biological systems? Here, I show that virulence evolution in experimental populations of bacteria and self-transmissible plasmids is best explained by within-host competition. Plasmids evolved to severely reduce the fitness of their hosts even in the absence of uninfected cells. This result is inconsistent with the trade-off hypothesis, which predicts that under these conditions vertically transmitted pathogens would evolve to be less virulent. Plasmid virulence was strongly correlated with the ability to superinfect cells containing competing plasmid genotypes, suggesting a key role for within-host competition. When virulent genotypes became common, hosts evolved resistance to plasmid infection. These results show that the trade-off hypothesis can incorrectly predict virulence evolution when within-host interactions are neglected. They also show that symbioses between bacteria and plasmids can evolve to be surprisingly antagonistic.

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                                    Ways to study microbial evolution

                                    Some choice nuggets I came across recently in a short perspective by Nadell and colleagues at Princeton:

                                    A recent paper in BMC Biology tests central elements of [social evolution] theory by manipulating a simple bacterial experimental system. This approach is useful for assessing the principles of social evolution, but we argue that more effort must be invested in the inverse problem: using social evolution theory to understand the lives of bacteria.

                                    The interaction between social evolution theory and microbiology holds enormous potential for enriching our knowledge of bacterial behavior, but to realize this potential we must ensure that information flows in both directions between these formerly disparate fields. At present, social evolution theory has benefited from simple experiments with bacteria, but microbiology has not equally benefited from social evolution theory.

                                    Pretty much sums up why I find some microbial cooperation papers really cool but others not so much.

                                    Nadell CD, Bassler BL, Levin SA (2008) Observing bacteria through the lens of social evolution. Journal of Biology 7:27. (doi:10.1186/jbiol87) http://jbiol.com/content/7/7/27

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                                      More heat than light

                                      Last summer, Nowak and colleagues published a perspective in Nature arguing against both the factual correctness and the scientific usefulness of kin selection theory.  It rubbed a lot of people the wrong way.  The paper has a lot of problems, but I’ll leave most of that argument to others.  Instead, here’s some correspondence I sent to Nature that they declined to publish:

                                      Nowak and coauthors (Nature 466, 1057-1062; 2010) claim that kin selection theory is limited by its inability to describe evolutionary dynamics and its requirement for weak selection, pairwise interactions, additive fitness effects, and specific kinds of population structure. Shortly before this article was published, my colleagues and I published an extension of kin selection theory that overcomes all of these issues (Science 328, 1700-1703; 2010). We agreed with Nowak and colleagues that many kin selection models are hard to apply to real data, but we took a different approach. Identifying and solving problems is a productive strategy of scientific inquiry; wholesale dismissal of an active research program is likely to generate more heat than light.

                                      jeff smith

                                      I think there are issues with kin selection theory very much along the lines that Nowak and company point out. But it’s not so much that they make the theory measurably wrong (on this point I disagree with the Nowak paper), but that they make it less useful, hard to apply, and easy to get wrong.

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                                        Starting again

                                        Starting in a new lab is a special time.  You have a space to choose what you’re going to be working on for the year or two.  It’s a nice position to be in, but it also creates its own kind of stress—I find myself thinking I’ve wasted the opportunity if I don’t come up with something OMG genius. Sometimes I think my theoretical ideas are more creative than experimental ones.  I think I do good empirical work, but it seems more… ordinary.

                                        At first I thought about projects that would fit with the scope of the lab’s current grants and would dovetail nicely with my previous work.  My advisors, though, said they believed the grant applications should only be seen as general guidelines for direction.

                                        Instead they asked, “What’s the coolest result you can get?”  They said they wanted me to publish a couple high-profile papers so I could get a good faculty job when I’m done.  I hadn’t really thought of it like that.  Not just the careerism aspect, but also explicitly going after the coolest, most compelling results possible.

                                        There’s a lot to be said for aiming high.  What are the most important problems that need to be solved?  What are the biggest outstanding issues?  What important pieces of the story have been neglected?  I’ve looked back at my “open questions” post a bunch.

                                        I also thought about the tools and techniques I want to learn here.  What do I want to do when I can finally work on the questions closest to my heart?

                                        In the end, the projects I’ve settled on seem pretty solid, even if they’re not exactly revolutionary.  There’s a good spread of general-principle issues that transcend specific systems, old-school animal behavior, neat modern techniques, experiment/theory interaction, and safety versus risk.  Now to the bench!

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